Simulations of current climate conditions serve to evaluate the performance
of RCMs. Since the SAR, a vast number of such simulations have been conducted
(McGregor, 1997; Appendices 10.1 to 10.3).
These fall into two categories, RCMs driven by observed (or “perfect”)
boundary conditions and RCMs driven by GCM boundary conditions. Observed boundary
conditions are derived from Numerical Weather Prediction (NWP) analyses (e.g.,
European Centre for Medium Range Weather Forecast (ECMWF) reanalysis, Gibson
et al. 1997; or National Center for Environmental Prediction (NCEP) reanalysis,
Kalnay et al., 1996). Over most regions they give accurate representation of
the large-scale flow and tropospheric temperature structure (Gibson et al.,
1997), although errors are still present due to poor data coverage and to observational
uncertainty. The analyses may be used to drive RCM simulations for short periods,
for comparison with individual episodes, or over long periods to allow statistical
evaluation of the model climatology. Comparison with climatologies is the only
available evaluation tool for RCMs driven by GCM fields, with the caveats applied
to GCM validation concerning the influence of sample size and decadal variability
(see Sections 10.2, 10.3, and 10.4).
Despite these, relatively short simulations (several years) can identify major
systematic RCM biases if they yield departures from observations significantly
greater than the observed natural variability (Machenhauer et al., 1996, 1998;
Christensen et al., 1997; Jones et al., 1999).

Often a serious problem in RCM evaluation is the lack of good quality high-resolution
observed data. In many regions, observations are extremely sparse or not readily
available. In addition, only little work has been carried out on how to use
point measurements to evaluate the grid-box mean values from a climate model,
especially when using sparse station networks or stations in complex topographical
terrain (e.g., Osborn and Hulme, 1997). Most of the observational data available
at typical RCM resolution (order of 50 km) is for precipitation and daily minimum
and maximum temperature. While these fields have been shown to be useful for
evaluating model performance, they are also the end product of a series of complex
processes, so that the evaluation of individual model dynamical and physical
processes is necessarily limited. Additional fields need to be examined in model
evaluation to broaden the perspective on model performance and to help delineate
sources of model error. Examples are the surface energy and water fluxes.

Despite these problems, the situation is steadily improving in terms of grid-cell
climatologies (Daly et al., 1994; New et al., 1999, 2000; Widman and Bretherton,
2000), with various groups developing high-resolution regional climatologies
(e.g., Christensen et al., 1998; Frei and Schär, 1998). In addition, regional
programs such as the Global Energy and Water Cycle Experiment (GEWEX) Continental-Scale
International Program (GCIP) have been designed with the purpose of developing
sets of observation databases at the regional scale for model evaluation (GCIP,
1998).

10.5.1.1 Mean climate: Simulations using analyses of observations

Ideally, experiments using analyses of observations to drive the RCMs should
precede any attempt to simulate climate change. The model behaviour, with realistic
forcing, should be as close as possible to that of the real atmosphere and experiments
driven by analyses of observations can reveal systematic model biases primarily
due to the internal model dynamics and physics.

A list of published RCM simulations driven by analyses of observations is given
in Appendix 10.1. Many of these studies present regional
differences (or biases) of seasonally or monthly-averaged surface air temperature
and precipitation from observed values. They indicate that current RCMs can
reproduce average observations over regions of size 105 to 106
km2 with errors generally below 2°C and within 5 to 50% of observed
precipitation, respectively (Giorgi and Shields, 1999; Small et al., 1999a,b;
van Lipzig, 1999; Pan et al., 2000). Uncertainties in the analysis fields, used
to drive the models, and, in the observed station data sets, should be considered
in the interpretation of these biases.

Various RCM intercomparison studies have been carried out to identify different
or common model strengths and weaknesses, over Europe by Christensen et al.
(1997), over the USA by Takle et al. (1999), and over East Asia by Leung et
al. (1999a). For Europe a wide range of performance was reported, with the better
models exhibiting a good simulation of surface air temperature (sub-regional
monthly bias in the range ±2°C), except over south-eastern Europe
during summer. For the USA, a major finding was that the model ability to simulate
precipitation episodes varied depending on the scale of the relevant dynamical
forcing. Organised synoptic-scale precipitation systems were well simulated
deterministically, while episodes of mesoscale and convective precipitation
were represented in a more stochastic sense, with less degree of agreement with
the observed events and among models. Over East Asia, a major factor in determining
the model performance was found to be the simulation of cloud radiative processes.